Diagnostic radiological imaging techniques are powerful noninvasive tools with which to identify normal and suspicious regions within the body. The use of multiparametric imaging methods, which can incorporate different functional radiological parameters for quantitative diagnosis, has been steadily increasing [e.g., see M. Filippi and R. I. Grossman, “MRI techniques to monitor MS evolution: the present and the future,” Neurology 58(8), 1147-1153 (2002); M. A. Jacobs, V. Stearns, A. C. Wolff, K. Macura, P. Argani, N. Khouri, T. Tsangaris, P. B. Barker, N. E. Davidson, Z. M. Bhujwalla, D. A. Bluemke, and R. Ouwerkerk, “Multiparametric magnetic resonance imaging, spectroscopy and multinuclear ((2)(3)Na) imaging monitoring of preoperative chemotherapy for locally advanced breast cancer,” Acad. Radiol. 17(12), 1477-1485 (2010)].
Current methods of lesion detection include dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and/or positron emission tomography/computed tomography (PET/CT) images, which generate large amounts of data. Therefore, image-processing algorithms are required to analyze these images and play a key role in helping radiologists to differentiate normal from abnormal tissue. In multiparametric functional radiological imaging, each image sequence or type provides different information about a tissue of interest and its adjacent boundaries [e.g., see M. A. Jacobs, R. A. Knight, H. Soltanian-Zadeth, Z. G. Zheng, A. V. Goussev, D. J. Peck, J. P. Windham, and M. Chopp, “Unsupervised segmentation of multiparameter MRI in experimental cerebral ischemia with comparison to T2, Diffusion, and ADC MRI parameters and histopathological validation,” J. Magn. Reson Imaging 11(4), 425-437 (2000); M. A. Jacobs, R. Ouwerkerk, A. C. Wolff, V. Stearns, P. A. Bottomley, P. B. Barker, P. Argani, N. Khouri, N. E. Davidson, Z. M. Bhujwalla, and D. A. Bluemke, “Multiparametric and multinuclear magnetic resonance imaging of human breast cancer: Current applications,” Technol Cancer Res. Treat. 3(6), 543-550 (2004)]. For example, in multiparametric breast MRI, diffusion-weighted imaging (DWI) and DCE MRI provide information about cellularity and the vascular profile of normal tissue and tissue with lesions.
Similarly, PET/CT data provide information on the metabolic state of tissue. However, combining these data sets can be challenging, and because of the multidimensional structure of the data, methods are needed to extract a meaningful representation of the underlying radiopathological interpretation.
Currently, most computer-aided diagnosis (CAD) can act as a second reader in numerous applications, such as breast imaging [e.g., see R. H. El Khouli, M. A. Jacobs, and D. A. Bluemke, “Magnetic resonance imaging of the breast,” Semin Roentgenol. 43(4), 265-281 (2008)]. Most CAD systems are based on pattern recognition and use Euclidean distances, correlation, or similar methods to compute similarity between structures in the data segmentation procedure. It has been shown, however, that methods based on Euclidean distance and other similarity measures cannot fully preserve the data structure, which negatively affects the performance of a CAD system.
Currently, there is limited technology with CAD systems that integrate multiparametric MRI and/or other radiological imaging procedures into highly specific dataset. For example, in breast cancer, typically, multiparametric MRI data consists of fat suppressed T2-weighted (T2WI), T1-weighted (T1WI), Dynamic Contrast Enhanced (DCE) and diffusion weighted imaging (DWI). These methods provide functional information that is currently not captured in existing CAD systems and CAD vendors do not use advanced machine learning methods for combining or visualizing the data. Moreover, the CAD systems use only small portions of the data for diagnosis. This limits the ability for a radiologist to be confident in gauging benign and malignant lesion boundaries and the extent of disease.
It thus would be desirable to provide new multiparametric non-linear Dimension reduction methods and systems for segmentation and classification of radiological images.